Update README.md

README.md

@@ -1,3 +1,151 @@
 ---
 license: apache-2.0
----
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-Coder-1.5B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- coder
+- mini
+- reasoning
+- o1
+---
+
+# Coder-o1-mini-reasoning
+
+A compact Python-focused reasoning model designed for coding assistance, debugging, code explanation, math reasoning, logic reasoning, Python concept explanation, and tool-style web search workflows.
+
+The model is intended for lightweight assistant use cases where users need clear explanations, step-by-step reasoning, beginner-friendly Python help, and practical debugging support.
+
+---
+
+## Capabilities
+
+This model can help with:
+
+* Python coding assistance
+* Python code explanation
+* Python debugging and error fixing
+* Python concept explanation
+* Basic to intermediate competitive programming
+* Math reasoning
+* Logic reasoning
+* Beginner-friendly programming guidance
+* General chat
+* Web search tool-call style conversations
+* Multi-turn coding discussion
+
+---
+
+## Chat Format
+
+The model follows a Harmony-style chat structure.
+
+Supported interaction flow:
+
+```text
+system -> developer -> user -> reasoning -> tool call -> tool result -> final response
+```
+
+For normal chat or coding use, you can use a standard chat-template style prompt.
+
+---
+
+## Basic Usage
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+MODEL_PATH = "kd13/Coder-o1-mini-reasoning"
+
+tok = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_PATH,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True
+)
+model.eval()
+
+if tok.pad_token is None:
+    tok.pad_token = tok.eos_token
+
+IM_END_ID = tok.convert_tokens_to_ids("<|im_end|>")
+if IM_END_ID is None or IM_END_ID == tok.unk_token_id:
+    IM_END_ID = tok.eos_token_id
+```
+
+---
+
+## Web Search Tool-Call Style
+
+The model can be used in tool-calling style conversations where the assistant decides when search is needed, emits a tool call, receives a tool result, and then writes the final answer.
+
+Example structure:
+
+```text
+system: You are a helpful assistant with access to web search.
+user: Find the latest information about a topic.
+assistant reasoning: Decide whether search is needed.
+assistant tool call: search(...)
+tool result: ...
+assistant final: Answer using the search result.
+```
+
+Actual tool execution depends on your inference framework or application wrapper.
+
+---
+
+## Recommended Use Cases
+
+This model is best suited for:
+
+* Python learning assistants
+* Coding tutor apps
+* Debugging helpers
+* Interview preparation
+* Beginner-to-intermediate Python problem solving
+* Math and logic explanation
+* Lightweight reasoning chatbots
+* Tool-call research experiments
+
+---
+
+## Limitations
+
+This model is not recommended for:
+
+* Very hard competitive programming problems
+* Advanced game theory problems
+* Complex graph theory or math-heavy algorithmic tasks
+* Production-critical software generation without review
+* Non-Python coding tasks such as C++, Java, Rust, Go, or JavaScript
+* Security-sensitive code generation
+* Medical, legal, or financial decision-making
+* Long multi-file software engineering tasks
+
+The model may sometimes:
+
+* Produce incorrect reasoning
+* Miss edge cases
+* Over-explain simple problems
+* Generate code that needs testing
+* Struggle with very long context
+* Use tool-call format inconsistently depending on the prompt
+
+Always test generated code before using it.
+
+---
+
+## License
+
+Please check the model repository license before commercial or production use.
+
+---
+
+## Disclaimer
+
+This model is an experimental small reasoning and coding assistant. It should be used as a helpful assistant, not as a guaranteed source of truth. For important tasks, verify outputs with tests, documentation, and human review.